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Data Scientist | https://zerowithdot.com | makes data make sense

On the benefits of building trust, precision, and awareness of one’s skills

I want to expand this work into a series of µ-tutorial videos. If you’re interested, please subscribe to my newsletter to stay in touch.

The tech world surrounds us. It penetrates nearly every aspect of our lives not only through devices and services that we use, but it also affects us in the way we think. Some of us chose to “marry” the technology, which became our professions. Some of us picked related careers in tech. Not everyone is a technology producer, but we are all consumers of it.

In my earlier article — “Technical skills that make you unsinkable…


Data, stones, and data and more stones…
Data, stones, and data and more stones…

I want to expand this work into a series of µ-tutorial videos. If you’re interested, please subscribe to my newsletter to stay in touch.

Introduction

Data analysis is one of the most essential steps in any data-related project. Regardless of the context (e.g. business, machine-learning, physics, etc.), there are many ways to get it right… or wrong. After all, decisions often depend on actual findings. and at the same time, nobody can tell you what to find before you have found it.

For these reasons, it is important to try to keep the process as smooth as possible. On one hand…


Simple usage of a reverse SSH tunnel for to greatly improve the usability.

Introduction

It’s been quite some time since we wrote on any “engineering-like” topic. As we all want to stay efficient and productive, it is a good time to revisit Google Colaboratory.

Google Colaboratory, or Colab for short, has been a great platform for data scientists or machine-learning enthusiasts in general. It offers a free instance of GPU and TPU for a limited time plus it serves a prettified version of a Jupyter notebook. It is a great combination for various smaller or mid-size projects.

Unfortunately, it comes with certain limitations. The biggest ones are the lack of storage persistency, as well…


Data analysis is by far one of the most frequently occurring tasks in both business and technology. It is the daily bread of thousands of academic researchers, bankers, engineers, and, obviously, data scientists. Interestingly, despite being such a popular activity, there seems to be no agreement on what should be the golden standard. It’s true that comparing e.g. a customer survey analysis with, let’s say, a hadron collider experiment outcome is at least, well… hard. It is also true that in either case, one can tell if the process has been carried well or poorly.

In this article, I would…


I want to expand this work into a series of µ-tutorial videos. If you’re interested, please subscribe to my newsletter to stay in touch.

There is probably no other field that changes as quickly and dynamically as technology. Tools and solutions often get obsolete in less than a decade and the reported lists of top in-demand skills update almost every quarter.

Data science is an example of a technology role that exploded almost like a supernova. It did not exist ten years ago (as of the writing of this article). …


Butterfly Effect, which has nothing to do with decision trees…, Poland 2020 (photo by the author).
Butterfly Effect, which has nothing to do with decision trees…, Poland 2020 (photo by the author).

I want to expand this work into a series of µ-tutorial videos. If you’re interested, please subscribe to my newsletter to stay in touch.

Introduction

It is not hard to be under an impression that the world is all about neural networks these days when it comes to making models. Many teams seem to brag about super-cool architectures as if getting enough quality data was straightforward, GPU racks were open 24/7 (for free), and their customer’s patience was set to infinity.

In this article, we will present one of the most basic machine-learning algorithms known as a Decision Tree. Decision trees…


A good day.
A good day.

Rarely do I get a feeling that there is not enough advice to go around. It’s quite the opposite. Advice is often free and accessible, despite not always being useful or applicable.

In this article, I would like to share some thoughts on what I think is useful. It is the product of my conclusions, which I have continuously been testing for the last four years. It works. It helped me, and I believe it will help you too.

The starting point

2016 was a turning year for my family. We lost our place to live, both got unemployed, and if that was…


Getting Started

With bare-bone numpy code.

This gentleman propagated forward backward. Cuba 2015.
This gentleman propagated forward backward. Cuba 2015.

I want to expand this work into a series of µ-tutorial videos. If you’re interested, please subscribe to my newsletter to stay in touch.

Introduction

Writing a custom implementation of a popular algorithm can be compared to playing a musical standard. For as long as the code reflects upon the equations, the functionality remains unchanged. It is, indeed, just like playing from notes. However, it lets you master your tools and practice your ability to hear and think.

In this post, we are going to re-play the classic Multi-Layer Perceptron. …


Why is time forecasting so challenging?

Introduction

I promised to myself not to write about Covid-19.

However, with my recent inclination in going back to fundamentals and revisiting some of the more interesting topics in mathematics, I thought it would be fun and useful to explain why forecasting a time series (e.g. a disease progression) is so challenging. More precisely, I want to explain why making such simulations can really be hard sometimes by showing how things work at the fundamental level.

We will start with some basic equations and discuss the main challenges that relate to data and building models. Then, we will move on to…


Simulation of stochastic differential equations using the Monte Carlo method and Python

Introduction

This is the second part of the work that attempts to find a recipe towards financial independence — a stage where you no longer need to work to support yourself.

In the previous article, we tried to formulate the problem of personal finance through a system of ordinary differential equations (ODE), which we later solved numerically using python. Given a set of input parameters, our numerical model was able to determine your financial condition [Github].

In this article, we bring it to the next stage. We add randomness to the equation to account for life’s unpredictability. …

Oleg Żero

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